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(小)出租车 (大)出租车车辆类型公交车

起始SOC 概率分布 N(0.5,0.1)N(0.5,0.1)N(0.3,0.12)N(0.3,0.12)N(0.3,0.12)N(0.3,0.1)N(0.4,0.12)N(0.6,0.12)N(0.6,0.12)N(0.6,0.1)

2222

第32卷

起始时间分布 充电功率/kW

电池容量/(kW?h) 96 96 64 64 64 64 32 32 32

均匀分布 135 均匀分布 21 直角梯形分布 7 直角梯形分布 7 均匀分布 21 均匀分布 21 均匀分布 7 N(19,1.52) 7

附录A

表A1 电动汽车充电行为相关参数设置 Tab. A1 Parameter settings of EVs charging load

calculation

车辆类型

充电次数/d

充电时段

是否有充电各时段充电时长限制

的概率 1.0 1.0

公务车 私家车(工作日)

N(9,0.52) 7 32

直角梯形分布 7

公交车

否 2 10:00—16:30 否 2 23:00—05:30

出租车(大)

2 02:00—05:00 120?min 1.0 否 2 11:30—14:30 否 2 02:00—04:00 否 2 11:30—14:00 否 1 18:00—07:00 否 1 07:30—17:00 否 1 19:00—07:00

1.0 1.0 1.0 1.0 0.2 0.7

出租车(小) 公务车 私家车 (工作日)

罗卓伟

收稿日期：2011-12-02。

作者简介：

罗卓伟(1984)，男，博士研究生，主要研究方向为电动汽车、电网优化控制与运行，luozhuowei10@gmail.com；

胡泽春(1979)，男，副教授，主要研究方向为智能电网、电力系统优化规划与运行；

宋永华(1964)，男，教授，英国皇家工程院院士，主要研究方向为智能电网和低碳电力。

1 19:00—22:00 80?min 0.1

(责任编辑 刘浩芳)

Extended Summary 正文参见pp.1-10

Study on Charging Load Modeling and Coordinated Charging of Electric

Vehicles Under Battery Swapping Modes

LUO Zhuowei, HU Zechun, SONG Yonghua, XU Zhiwei, JIA Long

(Tsinghua University)

KEY WORDS: battery distribution; battery swapping mode; coordinated charging; electric vehicle; inventory management

Both State Grid Corporation of China and China Southern Power Grid Corporation have both announced that they were going to promote battery swapping modes to refuel the electric vehicles (EVs) in 2011. Building the calculation and optimization model of EV charging load under the battery swap modes is of great significance to study the influence of charging load on power grids.

In this paper, the structure and operation of two kinds of battery swapping modes, i.e. charging-swapping modes and centralized charging unified battery distribution modes are analyzed first. Then take the battery swapping needs of customers as constraints, the first stage coordinated charging formulation is implemented to minimize the charging cost based on the time of use (TOU) electricity price mechanism. The delivery time and the (s, S) inventory management strategy are also considered in the centralized optimal charging model. In the second stage, the objective is to smooth the daily load curve fluctuation; and the peak load obtained from the first stage is set as the upper bound.

The coordination charging models proposed in this paper are taken as linear integer programming (LIP) problems, that can be solved by CPLEX toolbox.

Case studies simulate the charging loads of PEVs in the year of 2020 in China. The results are compared with those of the plug-in charging mode. Different kinds of battery swapping modes are utilized for different kinds of EVs, i.e. charging-swapping modes for public buses, centralized charging unified distribution modes for the rest kinds of vehicles. Based on the calculation results, shown in Fig.1 and Fig.2, the peak load of uncoordinated

Charging load/GW

6

Second stage Uncoordinated First stage optimizationcharging optimization

4

2

006:00

12:00

24:00 18:00 06:00

Time (b) Taxies

Charging load/GW

40302010

First stage optimization Uncoordinated charging optimization

006:00

12:00

18:00 06:0024:00 Time

(c) Private cars and official cars

Fig. 1 Coordinated and uncoordinated charging load curves of

the battery swapping mode

Charging load/GW

1?300

1?100

First stage optimization

optimization

900

Charging load/GW

3

Uncoordinated charging First stage

2

Second stage optimization

1

70006:00

12:00

18:00 06:0024:00 Time

Fig. 2 Total load curve considering grid base load with the

battery swapping mode

006:00

12:00 18:00 24:00

Time

(a) Buses

06:00

S1

charging scenario in battery swapping modes increases less than that of the plug-in mode. The peak load will not increase in the coordinated charging scenario under battery swapping modes. The results indicate that the proposed charging load optimization model can reduce the charging cost and smooth the load curve effectively.

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